WEBVTT - Uber Profit Grows and Adobe's Figma Deal Update

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<v Speaker 1>From Mahard.

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<v Speaker 2>We're Innovation of Money and Power Collie in Silicon Valley, NBN.

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<v Speaker 3>This is Bloomberg Technology with Caroline Hide and Ed Love Love.

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<v Speaker 4>No from Bloomberg's world headquarters in New York. Home Caroline

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<v Speaker 4>Hyde and.

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<v Speaker 5>In the Big Apple back together, I'm Ed Lovelow. This

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<v Speaker 5>is Bloomberg Technology coming up.

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<v Speaker 4>Full earnings coverage ahead. We're going to be breaking down

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<v Speaker 4>the results from Uber and sit down with the CEO

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<v Speaker 4>Daracosta Shahi.

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<v Speaker 5>Plus we'll speak to the CEO of Figma about where

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<v Speaker 5>Adobe's twenty billion dollar purchase of the company stands, and

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<v Speaker 5>take a look at new AI tools that they've unveiled.

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<v Speaker 4>And we'll bring you the big takeaways from open AI's

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<v Speaker 4>first ever developers conference as the AI startup on Veil's

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<v Speaker 4>custom GPTs. Stick into these Uber earnings for peace to

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<v Speaker 4>be welcoming to the show the CEO of the company,

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<v Speaker 4>Donticles Ashahi, as well as of course our own Emi

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<v Speaker 4>Chang of Blue magnes Ey.

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<v Speaker 6>Take it away first, Caroline, thank you so much, Dara,

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<v Speaker 6>thank you as well as always for joining us. So look,

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<v Speaker 6>Uber shares have almost doubled this year, but they were

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<v Speaker 6>a little volatile this morning on the upswing. Now, what's

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<v Speaker 6>the message to investors who are seeing mixed signals here?

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<v Speaker 2>Well, the message to investors is that we had another

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<v Speaker 2>record quarter and that the business is accelerating and actually

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<v Speaker 2>surprised us on on the top line, our growth bookings

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<v Speaker 2>came in over thirty five billion dollars, up twenty percent

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<v Speaker 2>on a constant currency basis, accelerated versus an already high

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<v Speaker 2>growth rate in Q two. If you look at the

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<v Speaker 2>number of trips taken on the platform, two point four

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<v Speaker 2>billion trips this last quarter, that's twenty seven million trips

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<v Speaker 2>taken every single day. That was up twenty five percent

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<v Speaker 2>on and you're on your basis again an acceleration over

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<v Speaker 2>last quarter where trips care twenty two percent. We did

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<v Speaker 2>have a revenue accounting adjustment, and basically it was taking

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<v Speaker 2>marketing costs and incentives. We do provide incentives to customers

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<v Speaker 2>discounts to spur demand, and while they used to be

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<v Speaker 2>classified as marketing cost in previous years, in previous quarters

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<v Speaker 2>they were classified as contra revenue and our revenue then

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<v Speaker 2>stated revenue grow ten percent. But this it's really the

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<v Speaker 2>geography of marketing classified as contra revenue took down a

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<v Speaker 2>reported revenue by eight percent doesn't affect the bottom line

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<v Speaker 2>at all. So our adjustedy BADAC came in at a

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<v Speaker 2>billion ninety two, up over one hundred percent on a

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<v Speaker 2>year on your basis. And then of course we were

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<v Speaker 2>again gap net income profitable with over two hundred million

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<v Speaker 2>dollars in net income and something that we expect to

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<v Speaker 2>be profitable going forward as an enterprise.

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<v Speaker 6>Right your second quarter of profitability. Revenue is growing more

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<v Speaker 6>slowly though over the last several quarters, so you know,

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<v Speaker 6>what are your longer term what's your longer term view

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<v Speaker 6>on where the growth is.

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<v Speaker 7>Going to come from?

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<v Speaker 6>And you know those who were concerned about market saturation, well, the.

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<v Speaker 2>Revenue growth slowdown is again purely as a result of

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<v Speaker 2>these accounting reclassifications. If you look at the business and

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<v Speaker 2>the bookings growth, which is really consumer demand, both on

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<v Speaker 2>the ride side and on the delivery side, both of

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<v Speaker 2>them accelerated. So our mobility business grew growth bookings thirty

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<v Speaker 2>percent on a year on year basis. It was an

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<v Speaker 2>acceleration over Q two. We saw strength all over the world,

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<v Speaker 2>but we saw particular strength in the Asia Pacific regions

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<v Speaker 2>and Latin American regions as a result of some of

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<v Speaker 2>the newer products that we've introduced, taxi two wheelers, motorcycles

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<v Speaker 2>that you know are Uber motorbikes that can whish through traffic,

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<v Speaker 2>et cetera. And then on the delivery, our gross bookings

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<v Speaker 2>grow sixteen percent, again an acceleration over Q two when

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<v Speaker 2>we grow fourteen percent, And that's just food delivery continuing

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<v Speaker 2>to be super popular with consumers all around the world.

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<v Speaker 2>It's our growing faster than the general category. We gain

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<v Speaker 2>category position in nine out of ten top ten markets

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<v Speaker 2>all around the world. And it's our number of grocery

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<v Speaker 2>offerings expanding beyond just food into grocery, alcohol, pet food,

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<v Speaker 2>whatever you want delivered to your home. We want Uber

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<v Speaker 2>East to be there. So the strength continues the consumer,

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<v Speaker 2>US consumer, global consumer continues to be strong, and that's

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<v Speaker 2>definitely been a tail one for our business.

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<v Speaker 6>You've been building out your advertising business, and I'm curious

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<v Speaker 6>where you see the biggest opportunity there. Who's a target demo,

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<v Speaker 6>where's the most upside, where's the best place to show

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<v Speaker 6>folks in the app new advertising and how does that

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<v Speaker 6>play out?

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<v Speaker 7>Well?

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<v Speaker 2>The biggest part of our advertising business us our restaurants

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<v Speaker 2>who are looking to improve their placement inside of the

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<v Speaker 2>marketplace by targeting consumers or eaters who are likely to

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<v Speaker 2>want to eat from that restaurant. And these are mostly

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<v Speaker 2>small and medium businesses and enterprises like McDonald's or Wendy's, etc.

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<v Speaker 2>Who advertise on our platform. And because we know so

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<v Speaker 2>much about our consumers, all our data, et cetera, we

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<v Speaker 2>can identify consumer segments who are likely to react, so

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<v Speaker 2>to speak, to that advertising. And as a result, advertising

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<v Speaker 2>revenue continues to grow at significant numbers. We targeted a

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<v Speaker 2>billion dollars in advertising for next year. We're pacing on

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<v Speaker 2>that target. We're going to exceed that target as well.

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<v Speaker 2>The number of advertisers who are advertising on Uber is

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<v Speaker 2>up seventy percent on a year on year basis. It's

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<v Speaker 2>over four hundred thousand dollars four hundred thousand businesses as well. Now,

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<v Speaker 2>one of the newer adverageising products that we're very excited

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<v Speaker 2>about is actually branded advertisements. And you might see those

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<v Speaker 2>branded advertisement advertisements when you're using your Uber app and

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<v Speaker 2>you're waiting for your car. We might show you and

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<v Speaker 2>add from an Apple or a credit card company or

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<v Speaker 2>an upcoming movie. Our demographic consumer tends to be urban,

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<v Speaker 2>tends to be very high income, and we have very

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<v Speaker 2>big hopes for our branded advertising business as well.

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<v Speaker 5>Dar We've gone deep here on the numbers, which Emily

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<v Speaker 5>and Caroline know I love doing.

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<v Speaker 7>But I just have a big picture question for you.

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<v Speaker 5>What does Uber look like in five years from now

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<v Speaker 5>and what are the steps that you take to meet

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<v Speaker 5>your vision for Uber in five years time?

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<v Speaker 2>Well, I think in five years time, we really want

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<v Speaker 2>to be that operating system for everyday life for you. Personally.

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<v Speaker 2>We want you to wake up and think about Uber.

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<v Speaker 2>Am I going to have breakfast? Am I going to

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<v Speaker 2>top up my grocery if I'm cooking tonight, going to

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<v Speaker 2>the office, coming back to the office. We really want

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<v Speaker 2>Uber to be there every day to help you in

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<v Speaker 2>your life, to get you where you're going, and to

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<v Speaker 2>save time from some of these tasks. You know, there

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<v Speaker 2>are some people who like grocery shopping, but there are

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<v Speaker 2>a ton of people who don't like grocery shopping. There

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<v Speaker 2>are a ton of people who just want to eat

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<v Speaker 2>at home, etc. And we want Uber to be that

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<v Speaker 2>relevant life partner or life operating system for you really

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<v Speaker 2>all over the world, it's.

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<v Speaker 4>The freight unit. Dara still part of that vision in

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<v Speaker 4>five years time. There's been talked from our own bluemerg analysis,

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<v Speaker 4>so maybe that's fun off well.

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<v Speaker 2>Freight is a very promising segment for us that is

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<v Speaker 2>suffering now from kind of the great freight recession that

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<v Speaker 2>you see create. Rates are coming down post pandemic as

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<v Speaker 2>a result of an oversupply situation. There are a ton

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<v Speaker 2>of digital brokers and actually analog traditional brokers going out

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<v Speaker 2>of business now. One of our or big competitors, Convoy,

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<v Speaker 2>for example, announced that they were going out of business

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<v Speaker 2>a couple of weeks ago, and that's those rates of

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<v Speaker 2>hit or freight business as well. It was down twenty

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<v Speaker 2>seven percent year on year. It lost some money, but

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<v Speaker 2>the technology of matching supply and demand in a digital

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<v Speaker 2>way we still think has incredible promise.

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<v Speaker 7>As the freight.

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<v Speaker 2>Business cleans up to some extent and some of the

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<v Speaker 2>smaller players move out, we think that Uber Freight is

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<v Speaker 2>going to emerge as one of the players with the

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<v Speaker 2>best technology out there. We've actually invested into the transportation

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<v Speaker 2>management part of the business, so not only do we

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<v Speaker 2>connect you with a trucker to go from point A

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<v Speaker 2>to B we're helping you with your whole freight network

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<v Speaker 2>and using machine learning, using the power of the data

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<v Speaker 2>that we have to create a more effective shipping network

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<v Speaker 2>for you. We think that's a very promising business and

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<v Speaker 2>at this point we are absolutely investing in freight and

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<v Speaker 2>innovating around freight.

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<v Speaker 6>Dara, Uber's been in the AI business for a long time,

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<v Speaker 6>but I know you're integrating it more and more into

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<v Speaker 6>the platform.

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<v Speaker 7>Where have you.

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<v Speaker 6>Seen the biggest value add so far and where do

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<v Speaker 6>you expect to see the biggest value add from AI

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<v Speaker 6>in the future.

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<v Speaker 2>Well, AI is intermingled into every part of our platform,

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<v Speaker 2>and you know, every single time you see a price

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<v Speaker 2>on Uber, every single time we make an offer to

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<v Speaker 2>a driver on Uber, every time we match you up

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<v Speaker 2>with a vehicle route you, all of that is powered

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<v Speaker 2>by AI. Some of the newer AI kind of innovations

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<v Speaker 2>that you see with large language models are helping us

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<v Speaker 2>automate some tasks that were difficult to automate previously or

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<v Speaker 2>difficult to train machine learning algos previously. One is, for example,

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<v Speaker 2>document transcription. If you put in your license, your insurance,

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<v Speaker 2>background checks, et cetera, documents we can transcribe to those

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<v Speaker 2>with the power of machine learning versus having humans look

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<v Speaker 2>at it. Humans, as it turns out, often make more

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<v Speaker 2>mistakes than algorithms. We actually combined both into a call

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<v Speaker 2>it a superhuman. Another area that we are we think

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<v Speaker 2>is quite promising is actually on the customer service side.

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<v Speaker 2>Anytime someone reaches out to us in customer service, we

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<v Speaker 2>have to be aware of you know, your history with Uber.

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<v Speaker 2>We've got to be aware of the context, what's going

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<v Speaker 2>on in that particular time, and also what are our policies,

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<v Speaker 2>and our policies differ all over the world. Again, machine

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<v Speaker 2>learning algorithms can go through all of that at the

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<v Speaker 2>click of the button essentially and then get back to

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<v Speaker 2>the customer, and we think actually provide an elevated customer

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<v Speaker 2>service at a lower cost, something that's super excited for us.

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<v Speaker 5>All right, Well, thanks there to Uber CEO Dara Kushashai

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<v Speaker 5>and of course Bloomberg exactly changing out in SF. Really

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<v Speaker 5>big morning actually on the Uber earnings front. A lot

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<v Speaker 5>of volatility in the stock, but a wide ranging conversation

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<v Speaker 5>are coming up here on Bloomberg technology. Open ai wraps

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<v Speaker 5>up its first ever developers conference in San Francisco. We

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<v Speaker 5>had the key takeaways from someone who is there on

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<v Speaker 5>the ground, a good friend of ours out in SF.

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<v Speaker 5>This is Bloomberg Technology.

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<v Speaker 8>GPTs are tailored versions of Chat GPT for a specific purpose.

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<v Speaker 8>You can build a GPT, a customized version of Chat

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<v Speaker 8>GPT for almost anything with instructions, expanded knowledge and actions,

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<v Speaker 8>and then you can publish it for others to use.

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<v Speaker 4>Open Ai CEO Sam Altman there at the company's first

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<v Speaker 4>ever developers conference where they unveiled custom versions of Chat GPT.

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<v Speaker 4>Let's go to Bloomberg's Rachel Metz. You are in the

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<v Speaker 4>building assessing all the euphoria around it, but ultimately is

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<v Speaker 4>this going to fend off the competition?

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<v Speaker 9>Rachel, That is a great question. I think that you're

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<v Speaker 9>starting to see them think about a little bit what

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<v Speaker 9>are other companies doing? But also what can we do

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<v Speaker 9>really well? How can we get people interested in using

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<v Speaker 9>our products even more for different things? And the fact

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<v Speaker 9>is that you can use something like chatchpt for all

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<v Speaker 9>sorts of things, but it would work even better if

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<v Speaker 9>you customized it and made it more something like a

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<v Speaker 9>homework helping bot or a recipe bot, you know, those

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<v Speaker 9>sorts of things, and give it a little extra data,

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<v Speaker 9>train it in a certain way, and perhaps it'll be

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<v Speaker 9>a little bit something like the app store that Apple

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<v Speaker 9>really came up with years ago, where people weren't quite

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<v Speaker 9>sure what to do it first with it right, and

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<v Speaker 9>then after a while it was like, ah, this is

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<v Speaker 9>what we do and people some things won't work, but

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<v Speaker 9>some things really well with.

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<v Speaker 5>That was something that a lot of folks are talking

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<v Speaker 5>about that caught their attentions. Open Ai to do a

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<v Speaker 5>digital store and introduce revenue sharing, kind of like X

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<v Speaker 5>is done in some ways the platformforming known as Twitter.

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<v Speaker 5>What do we learn about that future Open Ai store, Rachel.

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<v Speaker 9>We know that there is going to be some looking

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<v Speaker 9>over of the GPTs us they're calling them these customized

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<v Speaker 9>chatbots before people are allowed to offer them to other people,

0:13:20.000 --> 0:13:22.760
<v Speaker 9>similar to what Apple and Google et cetera do.

0:13:22.800 --> 0:13:24.600
<v Speaker 7>With their respective app stores.

0:13:24.920 --> 0:13:27.160
<v Speaker 9>It's not one hundred percent clear to me yet how

0:13:27.240 --> 0:13:30.160
<v Speaker 9>the revenue share is going to work in terms of people,

0:13:30.240 --> 0:13:32.920
<v Speaker 9>if people are downloading these things a lot, if the

0:13:33.000 --> 0:13:35.240
<v Speaker 9>creator is going to get paid a certain amount of money,

0:13:35.280 --> 0:13:37.040
<v Speaker 9>I think some of that stuff is still TBD.

0:13:38.240 --> 0:13:40.560
<v Speaker 4>We want to thank you Rachel Metz bringing all the

0:13:40.640 --> 0:13:43.520
<v Speaker 4>inside track what the event was like, ed Avity, Yeah.

0:13:43.320 --> 0:13:45.320
<v Speaker 5>We have a quick talking tech And first off, Elon

0:13:45.400 --> 0:13:49.320
<v Speaker 5>Musk's SpaceX is on track for fifteen billion dollars of

0:13:49.360 --> 0:13:52.480
<v Speaker 5>sales in twenty twenty four. That according to Bloomberg sources,

0:13:52.840 --> 0:13:56.440
<v Speaker 5>the company's Starlink internet business is showing growth such that

0:13:56.520 --> 0:14:00.880
<v Speaker 5>it's expected to outpace and exceed the company's rocket launch business.

0:14:01.160 --> 0:14:03.960
<v Speaker 5>Starlink could account for more than ten billion dollars in

0:14:03.960 --> 0:14:07.600
<v Speaker 5>total sales for next year, one source told me. Plus,

0:14:07.920 --> 0:14:10.360
<v Speaker 5>we Work shut as its doors, the company filing for

0:14:10.480 --> 0:14:14.920
<v Speaker 5>Chapter eleven bankruptcy in New Jersey, This capping a tumultuous

0:14:14.920 --> 0:14:17.720
<v Speaker 5>few years which the company has navigated a failed merger,

0:14:18.000 --> 0:14:21.320
<v Speaker 5>COVID nineteen lockdowns, a blank check merger, and of course

0:14:21.360 --> 0:14:24.360
<v Speaker 5>a slow return to office trend. We Work once commanded

0:14:24.400 --> 0:14:27.920
<v Speaker 5>a forty seven billion dollar valuation back in twenty nineteen,

0:14:28.280 --> 0:14:32.240
<v Speaker 5>but it has listed nineteen billion dollars of liabilities. And

0:14:32.280 --> 0:14:34.640
<v Speaker 5>then you look at who might be impacted as creditors.

0:14:34.680 --> 0:14:36.240
<v Speaker 5>Caro a big name on their.

0:14:36.440 --> 0:14:40.000
<v Speaker 4>Creditors credibility of one Massa Yoshi Son. Interestingly, here in

0:14:40.040 --> 0:14:43.400
<v Speaker 4>New York, they've already dumped forty New York City office

0:14:43.480 --> 0:14:47.200
<v Speaker 4>leases since that announcement, so notable moves already had, but

0:14:47.400 --> 0:14:49.320
<v Speaker 4>we've got some notable moves that are happening in the

0:14:49.360 --> 0:14:52.160
<v Speaker 4>worldern publicly traded companies and Apple's won to be keep

0:14:52.160 --> 0:14:53.200
<v Speaker 4>an eye on on Yeah.

0:14:53.000 --> 0:14:56.160
<v Speaker 5>And reported by who else Bloombo's Mark German a rare move,

0:14:56.200 --> 0:14:59.960
<v Speaker 5>but Apples basically hit pause on developing most of net

0:15:00.000 --> 0:15:03.680
<v Speaker 5>next year's software updates for iPhone, iPad, Mac and some

0:15:03.760 --> 0:15:07.880
<v Speaker 5>other devices because it's trying to fix some bugs. German

0:15:08.360 --> 0:15:11.080
<v Speaker 5>says that this delay has been announced internally to employees.

0:15:11.120 --> 0:15:13.160
<v Speaker 5>He's now reporting those details and we'll bring you more

0:15:13.200 --> 0:15:24.880
<v Speaker 5>on it. This is Blombog Technology. Figma is launching new

0:15:24.920 --> 0:15:28.160
<v Speaker 5>AI features for its collaborative tool fig Jam. The new

0:15:28.200 --> 0:15:32.680
<v Speaker 5>AI version aims to boost productivity with new features to generate, summarize,

0:15:32.720 --> 0:15:36.120
<v Speaker 5>and sort files, among other things. Delighted to be joined

0:15:36.320 --> 0:15:39.760
<v Speaker 5>by Figma's CEO Dylan Field and Caroline and I were

0:15:39.760 --> 0:15:43.160
<v Speaker 5>talking about this this morning about the technology base for

0:15:43.240 --> 0:15:46.680
<v Speaker 5>what you've announced. Where does the technology come from? Is

0:15:46.680 --> 0:15:49.440
<v Speaker 5>it your own large language model, foundation model powering this

0:15:49.880 --> 0:15:51.400
<v Speaker 5>or have you gone to a third party?

0:15:52.440 --> 0:15:54.160
<v Speaker 10>All right, well, it's great to be here and thank

0:15:54.160 --> 0:15:56.760
<v Speaker 10>you for having me. Just to back up a little bit,

0:15:57.240 --> 0:15:59.480
<v Speaker 10>Figma's platform. We try to make it step people can

0:15:59.520 --> 0:16:02.160
<v Speaker 10>go oh the way from idea to design to production

0:16:02.280 --> 0:16:06.080
<v Speaker 10>and software code. And what we're launching today is our

0:16:06.120 --> 0:16:08.840
<v Speaker 10>new fig Jmi features which will help it make it

0:16:08.880 --> 0:16:12.880
<v Speaker 10>so that you can do meetings better and have more

0:16:12.920 --> 0:16:17.080
<v Speaker 10>productive working sessions and brainstorms in big jam. And with

0:16:17.160 --> 0:16:20.960
<v Speaker 10>fig Jmi, we're really excited to see how people use it.

0:16:21.000 --> 0:16:24.160
<v Speaker 10>And we're using the our finacial model we're using is

0:16:24.160 --> 0:16:29.560
<v Speaker 10>from open Ai. We're using GPT four, but yeah, definitely

0:16:29.600 --> 0:16:32.600
<v Speaker 10>something that we could reconsider in the future. We have

0:16:32.640 --> 0:16:34.680
<v Speaker 10>done a lot of work on the prompt engineering particular

0:16:35.040 --> 0:16:36.760
<v Speaker 10>set we can make it set. We're able to better

0:16:36.800 --> 0:16:39.360
<v Speaker 10>lower the four and make it set more people are

0:16:39.360 --> 0:16:41.720
<v Speaker 10>able to have better working meetings, but also raise a

0:16:41.720 --> 0:16:43.200
<v Speaker 10>ceiling and make it so you can do more in

0:16:43.240 --> 0:16:44.520
<v Speaker 10>our product in fig jam.

0:16:44.920 --> 0:16:47.680
<v Speaker 4>So you've outlined basically how this is going to help customers.

0:16:48.000 --> 0:16:52.640
<v Speaker 4>Dnnan What does ultimately product design look like once AI

0:16:52.760 --> 0:16:56.240
<v Speaker 4>has really taken charge? I mean, where will the human

0:16:56.320 --> 0:16:59.040
<v Speaker 4>interact with AI? Where will ultimately be able to just

0:16:59.040 --> 0:17:01.560
<v Speaker 4>put in a prompt and how well the latest fintech

0:17:02.080 --> 0:17:05.679
<v Speaker 4>at built within a blink of an eye.

0:17:06.600 --> 0:17:08.080
<v Speaker 7>Yeah, I think that ultimately.

0:17:08.480 --> 0:17:11.240
<v Speaker 10>First of all, for fig JMAI, we're much more focused

0:17:11.280 --> 0:17:13.800
<v Speaker 10>on how do you take the busy work out of

0:17:13.880 --> 0:17:17.840
<v Speaker 10>creating great means and brainstorms. But if you think forward

0:17:17.840 --> 0:17:20.920
<v Speaker 10>to Figma design and how we're going to take AI

0:17:21.240 --> 0:17:23.320
<v Speaker 10>and put it into more of our products experience, whether

0:17:23.359 --> 0:17:26.399
<v Speaker 10>it's design or even development with dev Mode, the new

0:17:26.400 --> 0:17:31.119
<v Speaker 10>product we launched earlier this year, I can fig I

0:17:31.160 --> 0:17:34.399
<v Speaker 10>think that AI will really help you get to a

0:17:34.440 --> 0:17:38.080
<v Speaker 10>great first draft. It'll still take a great team to

0:17:38.119 --> 0:17:41.639
<v Speaker 10>get to that final product and it though, I think

0:17:41.680 --> 0:17:44.280
<v Speaker 10>though that it'll make it set you can do more

0:17:45.280 --> 0:17:49.080
<v Speaker 10>and be even more productive and efficient, and we're really

0:17:49.119 --> 0:17:52.160
<v Speaker 10>excited to see where that takes our users. But yeah,

0:17:52.240 --> 0:17:54.119
<v Speaker 10>right now, I think it's more about how do you

0:17:54.160 --> 0:17:56.719
<v Speaker 10>get away from the busy work, not how the robots

0:17:56.720 --> 0:17:57.080
<v Speaker 10>take over?

0:17:57.680 --> 0:17:58.159
<v Speaker 7>You cooled it.

0:17:58.160 --> 0:18:00.000
<v Speaker 5>By the way, when we were chatting Caroline, we were like,

0:18:00.680 --> 0:18:04.679
<v Speaker 5>which LM interesting choice on open AI. Look, it's been

0:18:04.720 --> 0:18:07.720
<v Speaker 5>a year, Dylan since you announced that you would be

0:18:07.720 --> 0:18:12.199
<v Speaker 5>acquired by Adobe. Is that actually going to happen? I mean,

0:18:12.200 --> 0:18:14.200
<v Speaker 5>what chances that the deal falls through?

0:18:15.160 --> 0:18:18.000
<v Speaker 10>We're very optimistic and really excited about what we can

0:18:18.000 --> 0:18:21.439
<v Speaker 10>do with Adobe. I think that there's such comminary strengths

0:18:21.480 --> 0:18:23.240
<v Speaker 10>that Adobe and Figma can bring together.

0:18:23.640 --> 0:18:24.360
<v Speaker 7>So we've seen with.

0:18:24.320 --> 0:18:27.080
<v Speaker 10>Adobe all their work with fire Orfly and what they've

0:18:27.119 --> 0:18:29.400
<v Speaker 10>done with AI, and Figma can wearn a lot from

0:18:29.440 --> 0:18:33.000
<v Speaker 10>Adobe there. But also Figma has gone into new audiences

0:18:33.040 --> 0:18:35.679
<v Speaker 10>such as developers with dev mode, and we think that

0:18:35.720 --> 0:18:38.439
<v Speaker 10>we can bring a lot to Adobe there as well.

0:18:38.680 --> 0:18:40.159
<v Speaker 10>And we can wait you get this done and do

0:18:40.280 --> 0:18:40.679
<v Speaker 10>or together.

0:18:41.760 --> 0:18:45.120
<v Speaker 4>You've already been brainstorming about you as a consolidated unit,

0:18:45.160 --> 0:18:48.920
<v Speaker 4>what actually you can bring to consumers, how your products change.

0:18:49.119 --> 0:18:52.200
<v Speaker 4>It's interesting actually the EU regulators, we're worrying about how

0:18:52.200 --> 0:18:55.760
<v Speaker 4>your use of AI might end up fending off competition

0:18:55.880 --> 0:18:58.560
<v Speaker 4>even further, how do you think AI brought together with

0:18:58.600 --> 0:19:01.080
<v Speaker 4>Adobe and yourself might change the game a little bit.

0:19:02.000 --> 0:19:04.800
<v Speaker 10>Look, I think that all companies will benefit from AI,

0:19:05.800 --> 0:19:09.040
<v Speaker 10>and I'm really excited to see how different companies are

0:19:09.080 --> 0:19:12.000
<v Speaker 10>able to bring AI into their solutions just to make

0:19:12.040 --> 0:19:16.600
<v Speaker 10>better products for everyone. So Adobe and Figma are no exceptions.

0:19:16.600 --> 0:19:20.400
<v Speaker 10>I think that both companies will do great things here.

0:19:20.440 --> 0:19:22.960
<v Speaker 10>And you know this is the very start. There's so

0:19:23.040 --> 0:19:24.720
<v Speaker 10>much more to come, and it's really hard to predict

0:19:25.200 --> 0:19:28.439
<v Speaker 10>the future, but obviously more building towards it.

0:19:28.440 --> 0:19:30.080
<v Speaker 7>It's hard to predict the future. I'm still going to

0:19:30.080 --> 0:19:30.919
<v Speaker 7>ask you to try.

0:19:31.680 --> 0:19:34.360
<v Speaker 5>There's a lot of interesting like Figma's own momentum, right,

0:19:34.400 --> 0:19:36.760
<v Speaker 5>That's why there was so much excitement about the deal

0:19:36.760 --> 0:19:40.399
<v Speaker 5>with Adobe. What is your customermentum? Like, what is it

0:19:40.520 --> 0:19:42.720
<v Speaker 5>you're carrying into this potential deal close?

0:19:44.040 --> 0:19:47.480
<v Speaker 10>Yeah, obviously I can't share exact numbers, but it's been

0:19:47.520 --> 0:19:50.680
<v Speaker 10>a very exciting your Figma. We've added I think about

0:19:50.680 --> 0:19:54.240
<v Speaker 10>five hundred employees now since the deal is announced. We've

0:19:54.480 --> 0:19:56.920
<v Speaker 10>introduced new products like dev Moode as well as advanced

0:19:56.920 --> 0:20:00.320
<v Speaker 10>pro typing variables and how the user conference called fig

0:20:00.400 --> 0:20:04.560
<v Speaker 10>with eighty five hundred people coming. Honestly, it's it's felt

0:20:04.600 --> 0:20:08.800
<v Speaker 10>like we've been accelerating our pace and I hope to

0:20:09.040 --> 0:20:12.400
<v Speaker 10>continue that acceleration and momentum into the deal.

0:20:12.400 --> 0:20:12.800
<v Speaker 7>Clothes.

0:20:13.240 --> 0:20:15.120
<v Speaker 4>Well, really nice to have you here talking us through

0:20:15.119 --> 0:20:17.760
<v Speaker 4>at Figma. CEO Dylan Field, thanks so much for your

0:20:17.760 --> 0:20:18.280
<v Speaker 4>time today.

0:20:25.680 --> 0:20:27.280
<v Speaker 5>Welcome back to Bloomberg Technology.

0:20:27.320 --> 0:20:29.640
<v Speaker 7>I'm Ed Loveloke and I'm Caroline Hyde.

0:20:29.680 --> 0:20:31.080
<v Speaker 4>One of those days, let's get a quick check on

0:20:31.080 --> 0:20:33.040
<v Speaker 4>the markets at the moment, because we're on a bit

0:20:33.080 --> 0:20:34.800
<v Speaker 4>of a tear, we've got desire to get into tech

0:20:34.840 --> 0:20:37.080
<v Speaker 4>names in particular. In fact, we have been up for

0:20:37.200 --> 0:20:40.359
<v Speaker 4>eight straight days on the NASDAC and indeed on the

0:20:40.400 --> 0:20:43.520
<v Speaker 4>NASDAQ one hundred longest winning streets. It's November twenty twenty one,

0:20:43.720 --> 0:20:45.520
<v Speaker 4>whilso adding the fuel to the far while we up

0:20:45.560 --> 0:20:47.200
<v Speaker 4>more than eight percent. Because some of the big tech

0:20:47.280 --> 0:20:49.359
<v Speaker 4>names are rising on the day, let's look over and

0:20:49.400 --> 0:20:51.359
<v Speaker 4>look at some of the micro names, and in fact,

0:20:51.400 --> 0:20:53.840
<v Speaker 4>Microsoft heading over towards you, had a new record high.

0:20:53.840 --> 0:20:55.720
<v Speaker 4>At the moment, we're currently up by just four and

0:20:56.119 --> 0:20:57.959
<v Speaker 4>three quarters of point, but that's still one point three

0:20:58.000 --> 0:21:01.479
<v Speaker 4>percent to the upside. Data Dog absolutely flying New York

0:21:01.520 --> 0:21:02.720
<v Speaker 4>based company up thirty percent.

0:21:02.800 --> 0:21:03.360
<v Speaker 7>Let's call it.

0:21:03.560 --> 0:21:06.040
<v Speaker 4>As we see this software company outperform, really raising its

0:21:06.040 --> 0:21:08.320
<v Speaker 4>guidance on its back of its numbers uber as well

0:21:08.560 --> 0:21:10.800
<v Speaker 4>managing to be on the higher site after its earnings

0:21:10.840 --> 0:21:13.600
<v Speaker 4>as well, we see that it's really pushing towards well,

0:21:13.640 --> 0:21:16.439
<v Speaker 4>the forecast being one that the investor bases liking were

0:21:16.520 --> 0:21:19.560
<v Speaker 4>up two point four percent, and Apple still higher more

0:21:19.560 --> 0:21:21.600
<v Speaker 4>than percent point higher. That's as we get a scoop

0:21:21.640 --> 0:21:23.399
<v Speaker 4>coming from our own Mark German, who's saying, look, they

0:21:23.480 --> 0:21:26.480
<v Speaker 4>might have to put pause to some software initiatives and

0:21:26.520 --> 0:21:29.280
<v Speaker 4>indeed's improvements next year because as they want to iron

0:21:29.280 --> 0:21:32.040
<v Speaker 4>out some glitches in particular that will be across their phone,

0:21:32.080 --> 0:21:35.240
<v Speaker 4>that imag across the macro course and increase across most

0:21:35.240 --> 0:21:37.560
<v Speaker 4>of their software. But what'll be looking at in terms

0:21:37.600 --> 0:21:38.680
<v Speaker 4>of individual moves today.

0:21:38.800 --> 0:21:41.160
<v Speaker 5>Yeah, Look, some of the biggest names in megacat tech

0:21:41.280 --> 0:21:44.040
<v Speaker 5>are kind of in the spotlight this week for political reasons.

0:21:44.040 --> 0:21:47.880
<v Speaker 5>Officials from Meta, Google, Microsoft, and Adobe, we're all heading

0:21:47.880 --> 0:21:51.720
<v Speaker 5>to Capitol Hill tomorrow for another Senate forum on AI's

0:21:51.760 --> 0:21:55.440
<v Speaker 5>growing influence on American elections and democracy. The forum's part

0:21:55.440 --> 0:21:58.800
<v Speaker 5>of a series of closed door discussions that Senate Majority

0:21:58.840 --> 0:22:02.200
<v Speaker 5>Leader Chuck Schumer is been leading September to help members

0:22:02.240 --> 0:22:04.800
<v Speaker 5>get up to speed on the technology and in the end,

0:22:05.119 --> 0:22:07.560
<v Speaker 5>regulate it. We're going to dive deep into this conversation

0:22:07.680 --> 0:22:12.399
<v Speaker 5>on this election day with Anjana Sosala, Professor of Responsible

0:22:12.440 --> 0:22:16.080
<v Speaker 5>AI at Michigan State University. The way that we frame

0:22:16.119 --> 0:22:19.080
<v Speaker 5>it in our Daily Politics newsletter is that today's election

0:22:19.680 --> 0:22:21.920
<v Speaker 5>is kind of a testing ground for the events of

0:22:21.960 --> 0:22:25.520
<v Speaker 5>twenty twenty four and in the context of technology companies

0:22:25.520 --> 0:22:29.280
<v Speaker 5>and social media companies, do you see it that way, Professor.

0:22:29.760 --> 0:22:33.840
<v Speaker 11>Yes, absolutely. I think this is a test of multiple things.

0:22:34.119 --> 0:22:40.120
<v Speaker 11>One is the provenance of some of the methods used

0:22:40.119 --> 0:22:45.280
<v Speaker 11>to identify deep fakes. And also misinformation has gotten very

0:22:45.320 --> 0:22:50.600
<v Speaker 11>sophisticated in today's world, So how can platforms enforce all

0:22:50.600 --> 0:22:56.000
<v Speaker 11>these policies where given the volume of misinformation that's out there,

0:22:57.760 --> 0:23:02.720
<v Speaker 11>what are some sort of content moderation policies that platforms

0:23:02.760 --> 0:23:04.080
<v Speaker 11>can responsibly implement?

0:23:04.800 --> 0:23:08.239
<v Speaker 4>And to that end, how much can artificial intelligence bea

0:23:08.400 --> 0:23:11.200
<v Speaker 4>something that fights it as well as ads fuel to

0:23:11.320 --> 0:23:13.480
<v Speaker 4>the fire ultimately onsider which side is going to be

0:23:13.480 --> 0:23:16.320
<v Speaker 4>winning out here? How can wese artificial intelligence to help

0:23:16.480 --> 0:23:18.080
<v Speaker 4>whites off this rise and misinformation.

0:23:19.520 --> 0:23:23.320
<v Speaker 11>Well, there's definitely a lot of methods that platforms have

0:23:23.520 --> 0:23:29.320
<v Speaker 11>used meta for example, users these methods called self supervised

0:23:29.359 --> 0:23:34.720
<v Speaker 11>learning to identify deep fakes and fake news. And sometimes

0:23:34.720 --> 0:23:37.639
<v Speaker 11>what happens is there'll be issues where we have a

0:23:37.720 --> 0:23:41.680
<v Speaker 11>known source of misinformation and it's debunked and removed from

0:23:41.680 --> 0:23:45.600
<v Speaker 11>the platform. But they can be very easy sort of

0:23:46.880 --> 0:23:52.520
<v Speaker 11>you can duplicate essentially the same information and spread it

0:23:52.560 --> 0:23:54.880
<v Speaker 11>in a different way. And so platforms have gotten good

0:23:54.880 --> 0:23:57.800
<v Speaker 11>at those kinds of enforcement issues. So AI has helped

0:23:57.840 --> 0:24:01.960
<v Speaker 11>tremendously in those kinds of cracking down on some known

0:24:02.000 --> 0:24:05.679
<v Speaker 11>sources of misinformation. What is I think challenging from an

0:24:05.720 --> 0:24:11.159
<v Speaker 11>AI perspective is things where there are half truths or

0:24:12.400 --> 0:24:17.840
<v Speaker 11>rumors that can lead to maybe people not showing up

0:24:17.880 --> 0:24:23.920
<v Speaker 11>in voting, like active you know, water turnout, disinformation effects.

0:24:24.320 --> 0:24:26.320
<v Speaker 11>I think as are challenges for AI.

0:24:26.920 --> 0:24:30.320
<v Speaker 5>Professor Caroline and I have covered election cycles and we've

0:24:30.359 --> 0:24:34.560
<v Speaker 5>talked about social media companies be it meta x formerly

0:24:34.640 --> 0:24:38.240
<v Speaker 5>known as Twitter, and in that period they have talked

0:24:38.280 --> 0:24:40.800
<v Speaker 5>to us about the actions they're taking on content moderation,

0:24:41.200 --> 0:24:45.480
<v Speaker 5>but also said that it's not their responsibility to oversee everything. Essentially,

0:24:46.240 --> 0:24:48.160
<v Speaker 5>what's interesting about AI is it gives them a new

0:24:48.160 --> 0:24:51.520
<v Speaker 5>technological tool. Is it a sufficient tool for them to

0:24:51.560 --> 0:24:53.200
<v Speaker 5>get it right this time around?

0:24:54.400 --> 0:24:57.679
<v Speaker 11>I think it's only one part of the arsenal of

0:24:57.800 --> 0:25:01.720
<v Speaker 11>tools that they have, and in the sense that you know,

0:25:01.760 --> 0:25:06.320
<v Speaker 11>it's not completely maybe platforms responsibility alone, because we also

0:25:06.560 --> 0:25:11.840
<v Speaker 11>need a sort of a coalition of citizens and platforms

0:25:11.880 --> 0:25:17.080
<v Speaker 11>working together to solve the larger challenge of disinformation and misinformation,

0:25:17.400 --> 0:25:21.919
<v Speaker 11>especially given it's so easy to create and manipulate the

0:25:22.040 --> 0:25:27.000
<v Speaker 11>truth and so easy to spread you know, aisstate, deep

0:25:27.040 --> 0:25:28.040
<v Speaker 11>figs and so forth.

0:25:28.920 --> 0:25:32.119
<v Speaker 4>I want to also bring to our audiences awareness that,

0:25:32.240 --> 0:25:34.720
<v Speaker 4>of course today is not just a bad election and

0:25:34.760 --> 0:25:36.879
<v Speaker 4>the focus, but it still continues to be one where

0:25:37.440 --> 0:25:40.400
<v Speaker 4>particularly the government is looking at the impact of social

0:25:40.480 --> 0:25:43.480
<v Speaker 4>media or mental health, particularly those of teens and younger

0:25:43.480 --> 0:25:45.840
<v Speaker 4>adults on genre and at the moment we understand a

0:25:45.880 --> 0:25:48.600
<v Speaker 4>form of Facebook employee and whistleblower, our turo Bajar is

0:25:48.600 --> 0:25:52.040
<v Speaker 4>actually testifying before the Senate Subcommittee about social media, it's

0:25:52.080 --> 0:25:54.880
<v Speaker 4>impact on team mental health, the crisis there and MET

0:25:54.960 --> 0:25:57.240
<v Speaker 4>just put out statements today saying every day countless people

0:25:57.240 --> 0:25:59.399
<v Speaker 4>inside and outside of matter are working on how to

0:25:59.440 --> 0:26:02.800
<v Speaker 4>help keep young people safe online? How do you wait

0:26:02.880 --> 0:26:07.040
<v Speaker 4>them at this moment fighting that particular areas of concern

0:26:07.200 --> 0:26:08.600
<v Speaker 4>that society has a lot.

0:26:09.800 --> 0:26:13.600
<v Speaker 11>So this is actually a somewhat of a troubling area

0:26:14.080 --> 0:26:16.480
<v Speaker 11>because I think there were a lot of studies where

0:26:16.520 --> 0:26:20.320
<v Speaker 11>people have tried to create, you know, an account posing

0:26:20.400 --> 0:26:24.480
<v Speaker 11>as a teenager and the first within the seven or

0:26:24.560 --> 0:26:28.800
<v Speaker 11>eight you know, sponsored ads, you see your clicks that

0:26:29.800 --> 0:26:32.560
<v Speaker 11>you will end up seeing content that is kind of

0:26:32.680 --> 0:26:36.159
<v Speaker 11>very troubling from a treteam mental health perspective. And so

0:26:36.200 --> 0:26:40.000
<v Speaker 11>I think those are things where once again we need

0:26:40.040 --> 0:26:44.880
<v Speaker 11>more transparency reports. Europe is doing that, asking platforms to

0:26:44.920 --> 0:26:49.679
<v Speaker 11>provide more detail into how they're enforcing all these policies.

0:26:50.280 --> 0:26:54.399
<v Speaker 11>So I think that's this is one area where maybe

0:26:54.400 --> 0:26:58.360
<v Speaker 11>platforms also have a you know, a huge challenge. So

0:26:58.840 --> 0:27:01.359
<v Speaker 11>I'm not you know, I'm sure they are doing a

0:27:01.400 --> 0:27:06.080
<v Speaker 11>lot from their end, but as citizens and ASLAM when

0:27:06.119 --> 0:27:09.639
<v Speaker 11>the lawmakers have a lot of questions, I think we

0:27:09.800 --> 0:27:13.560
<v Speaker 11>need to ask platforms to be more truthful and transparent

0:27:13.720 --> 0:27:16.880
<v Speaker 11>and share with us all their efforts and in content

0:27:17.000 --> 0:27:20.040
<v Speaker 11>moderation how they're fighting all these issues.

0:27:20.400 --> 0:27:22.359
<v Speaker 4>And Jana, it's great to have your expertise on the show.

0:27:22.520 --> 0:27:25.280
<v Speaker 4>Thank you, come back. And Joanna Sosala, their professor of

0:27:25.280 --> 0:27:28.160
<v Speaker 4>Responsible AI at Michigan State University, we thank her.

0:27:28.160 --> 0:27:28.920
<v Speaker 7>Meanwhile, coming up.

0:27:29.080 --> 0:27:32.840
<v Speaker 4>Now, how to accelerate workflows for financial services businesses with

0:27:33.400 --> 0:27:36.159
<v Speaker 4>your guest AI. We will be joined by Il Shiner,

0:27:36.480 --> 0:27:39.280
<v Speaker 4>CEO of the AI platform Black Hall, coming out of Stealthbed.

0:27:39.640 --> 0:27:42.200
<v Speaker 5>Yeah, I'm still with the earning story as well. Global Foundaries,

0:27:42.200 --> 0:27:44.000
<v Speaker 5>a stock that's up seven percent, on track for its

0:27:44.040 --> 0:27:47.240
<v Speaker 5>biggest jump since February. Be Expectations in the court have

0:27:47.320 --> 0:27:51.920
<v Speaker 5>gone strong EPs forecasts for the current period, slightly disappointing

0:27:51.960 --> 0:27:52.760
<v Speaker 5>revenue forecasts.

0:27:52.760 --> 0:27:53.919
<v Speaker 7>And what is it talking about?

0:27:54.480 --> 0:27:57.680
<v Speaker 5>Uncertain economic and geopolitical situation around the world. One of

0:27:57.720 --> 0:28:00.280
<v Speaker 5>the smaller contract manufacturers for chips that we like to

0:28:00.359 --> 0:28:10.640
<v Speaker 5>track this has been by Technology.

0:28:16.720 --> 0:28:18.800
<v Speaker 4>It's an AI startup world and we're just living in it.

0:28:18.880 --> 0:28:21.720
<v Speaker 4>One more player comes to like black Or coming out

0:28:21.720 --> 0:28:25.040
<v Speaker 4>of Stealth, new AI platform for financial services in particular,

0:28:25.080 --> 0:28:28.280
<v Speaker 4>it's just launched today announcing sixty million dollars in financing

0:28:28.560 --> 0:28:31.000
<v Speaker 4>led by another than a sixteen z an Oak eight

0:28:31.080 --> 0:28:33.800
<v Speaker 4>c FT plus a whole list of the who's who's

0:28:33.800 --> 0:28:37.080
<v Speaker 4>a VC. Let's bring in Black Hole CEO al Shana,

0:28:37.280 --> 0:28:39.800
<v Speaker 4>who is going to discuss, well, ultimately, what you're using

0:28:39.840 --> 0:28:42.920
<v Speaker 4>sixty million dollars for? What problem are you solving with

0:28:43.000 --> 0:28:45.240
<v Speaker 4>a much hyped area of artificial intelligence?

0:28:46.720 --> 0:28:50.040
<v Speaker 3>Thank you for having me. We we're very excited to

0:28:50.040 --> 0:28:56.480
<v Speaker 3>announce our launch out of Stealth. We're using we're building

0:28:56.480 --> 0:29:00.800
<v Speaker 3>a platform by leveraging AI. Is that streamline an end

0:29:00.840 --> 0:29:02.960
<v Speaker 3>to end of all of the co workflow of the

0:29:03.000 --> 0:29:05.800
<v Speaker 3>Black Office of CPS at the moment, and then are

0:29:05.840 --> 0:29:09.360
<v Speaker 3>on financial services in general. Most of the problems that

0:29:09.400 --> 0:29:12.640
<v Speaker 3>we focus on now is the wrong capacity issues for

0:29:12.720 --> 0:29:16.600
<v Speaker 3>CPA firms where there are higher complexity of the tax

0:29:16.640 --> 0:29:20.160
<v Speaker 3>code two hundred and thirty eight million tax returns done

0:29:20.200 --> 0:29:23.320
<v Speaker 3>annually in the US, but at the same time, three

0:29:23.360 --> 0:29:27.760
<v Speaker 3>hundred thousand accountants left the job in the last two

0:29:27.840 --> 0:29:31.840
<v Speaker 3>years and there are sixteen percent less accountant than twenty eighteen,

0:29:32.520 --> 0:29:37.200
<v Speaker 3>so they're facing very severe capacity issues and the giving

0:29:37.280 --> 0:29:39.360
<v Speaker 3>up on revenue and other things that they went to

0:29:39.400 --> 0:29:42.000
<v Speaker 3>school in order to the study accounting to solve for

0:29:42.480 --> 0:29:45.680
<v Speaker 3>instead of that doing very manyal work. So what we're

0:29:45.680 --> 0:29:48.160
<v Speaker 3>doing is we're automating all of the pieces that are

0:29:48.680 --> 0:29:51.760
<v Speaker 3>could be automated by a machine, and right now are

0:29:51.800 --> 0:29:56.240
<v Speaker 3>struggling to find the right talent for the capacity constraint.

0:29:56.880 --> 0:30:02.320
<v Speaker 5>I'll found this fun coast la Andrey general catalyst that's

0:30:02.360 --> 0:30:05.280
<v Speaker 5>like the Who's who? A VC is backing you and

0:30:05.320 --> 0:30:07.440
<v Speaker 5>you did it coming out of STEPH. I don't understand

0:30:07.920 --> 0:30:10.520
<v Speaker 5>how does it work if you're in self? How do

0:30:10.560 --> 0:30:13.320
<v Speaker 5>they even know about you? Or is it you kicking

0:30:13.360 --> 0:30:16.320
<v Speaker 5>down doors and saying we have something really big coming.

0:30:18.560 --> 0:30:21.760
<v Speaker 3>We do have something really big coming, but we do

0:30:21.880 --> 0:30:27.800
<v Speaker 3>have an existing relationship with most of those investors. In

0:30:27.840 --> 0:30:30.800
<v Speaker 3>my previous company, some of them were already investors. So

0:30:30.880 --> 0:30:33.640
<v Speaker 3>when I started to work on this problem, I reached

0:30:33.680 --> 0:30:36.840
<v Speaker 3>out and we started discussions and we didn't have to

0:30:36.920 --> 0:30:37.880
<v Speaker 3>kick too many doors.

0:30:39.520 --> 0:30:40.800
<v Speaker 7>Talk to us about the technology.

0:30:41.000 --> 0:30:44.280
<v Speaker 5>Are you a maker of foundation or large language models,

0:30:44.840 --> 0:30:47.920
<v Speaker 5>or do you use the underlying technology of a third

0:30:48.000 --> 0:30:51.200
<v Speaker 5>party to then make an AI tool that's relevant in

0:30:51.240 --> 0:30:52.680
<v Speaker 5>the financial services space.

0:30:54.720 --> 0:30:58.520
<v Speaker 3>We do build a proprietary technology that consists not just

0:30:58.600 --> 0:31:02.800
<v Speaker 3>from L and M, but from a combination of machine learning,

0:31:02.880 --> 0:31:08.240
<v Speaker 3>automation vision. We do use third party technology for the LLM.

0:31:08.280 --> 0:31:10.760
<v Speaker 3>We made a strategic decision not to build them model

0:31:10.840 --> 0:31:15.120
<v Speaker 3>yet because it's extremely expensive. But with the pace of progress,

0:31:15.160 --> 0:31:16.600
<v Speaker 3>we believe that in two years it will be a

0:31:16.600 --> 0:31:19.440
<v Speaker 3>fraction of the cost to build it ourself. But ninety

0:31:19.440 --> 0:31:22.520
<v Speaker 3>percent of the technology is not leveraging LM, which which

0:31:23.080 --> 0:31:29.520
<v Speaker 3>proprietary technology. We're using the usual suspects, but it's relatively

0:31:29.840 --> 0:31:31.920
<v Speaker 3>less than ten percent of what we actually offering. It's

0:31:32.040 --> 0:31:32.880
<v Speaker 3>end product today.

0:31:33.120 --> 0:31:36.000
<v Speaker 4>So tell us about your moat Ultimately, how do you

0:31:36.120 --> 0:31:39.960
<v Speaker 4>ensure that anyone who wants to be really making their

0:31:40.000 --> 0:31:43.240
<v Speaker 4>accounting work, making the job of a CPA efficient, only

0:31:43.240 --> 0:31:46.600
<v Speaker 4>comes to black or not others that are building similar models.

0:31:47.600 --> 0:31:49.400
<v Speaker 3>That's the main reason why we were in Stell for

0:31:49.480 --> 0:31:52.560
<v Speaker 3>so long. We spend two years building the architecture, building

0:31:52.600 --> 0:31:55.480
<v Speaker 3>the pipes, building the actual plumbing, or how we put

0:31:55.520 --> 0:31:59.600
<v Speaker 3>the data, how we train a model to recognize the document,

0:31:59.680 --> 0:32:03.400
<v Speaker 3>what I of a document classified automatically fetching the data

0:32:03.480 --> 0:32:07.200
<v Speaker 3>from within the different document. They could be handwritten, they

0:32:07.240 --> 0:32:09.760
<v Speaker 3>could be a PDF, they could be JPEG, and we

0:32:09.840 --> 0:32:11.640
<v Speaker 3>put it in the right place in the tax software

0:32:11.680 --> 0:32:14.840
<v Speaker 3>without requiring the CPA to change any workflow any tax

0:32:14.880 --> 0:32:19.560
<v Speaker 3>doctors using today, and eventually the end product is a

0:32:19.600 --> 0:32:22.479
<v Speaker 3>fully completed tax return ready for the CPA to review,

0:32:22.960 --> 0:32:26.480
<v Speaker 3>as well as the work products that associate without like

0:32:26.560 --> 0:32:31.120
<v Speaker 3>tie out, binder and so on. It does take more

0:32:31.120 --> 0:32:34.640
<v Speaker 3>than AI to build that, a lot of inter understanding

0:32:34.680 --> 0:32:37.920
<v Speaker 3>of the workflow and the processes, and we started relatively

0:32:37.920 --> 0:32:41.000
<v Speaker 3>early on working with many different CPA firms and tax

0:32:41.040 --> 0:32:44.080
<v Speaker 3>preparation firm to make sure that we understand the workflow

0:32:44.120 --> 0:32:48.200
<v Speaker 3>end to end, even before it was cool to use

0:32:48.240 --> 0:32:51.320
<v Speaker 3>the word AI. We automated very big piece of that.

0:32:52.840 --> 0:32:55.280
<v Speaker 5>You're a serial entrepreneur and founder. You've been on this

0:32:55.320 --> 0:32:58.280
<v Speaker 5>program with a different name, fund Box. I've written about

0:32:58.280 --> 0:33:01.360
<v Speaker 5>FunBox and it's fundraising. Yeah, so you've got a pretty

0:33:01.400 --> 0:33:04.720
<v Speaker 5>good lay of the land right now. How expensive is

0:33:04.800 --> 0:33:07.240
<v Speaker 5>engineering talent? Like, I know you have a big chunk

0:33:07.240 --> 0:33:09.320
<v Speaker 5>of cash now, but how far is it going to go?

0:33:09.680 --> 0:33:10.520
<v Speaker 7>In the talent.

0:33:10.320 --> 0:33:17.280
<v Speaker 3>Space, engineers are still expensive. I think, just like CPAs

0:33:19.160 --> 0:33:21.800
<v Speaker 3>when you want to focus on the very high caliber

0:33:21.840 --> 0:33:26.920
<v Speaker 3>of talent. Humans are still in high demand and very expensive,

0:33:27.920 --> 0:33:30.800
<v Speaker 3>and especially when you start adding words like data science

0:33:30.800 --> 0:33:34.920
<v Speaker 3>and AI, it makes them even more expensive. But the

0:33:34.960 --> 0:33:37.640
<v Speaker 3>bottom line is that if you build the right team,

0:33:38.040 --> 0:33:40.200
<v Speaker 3>you don't need to throw too much money on the problem.

0:33:40.240 --> 0:33:43.080
<v Speaker 3>You don't want to higher before you have a product

0:33:43.080 --> 0:33:44.920
<v Speaker 3>out there, and once you have the product, you need

0:33:44.960 --> 0:33:48.280
<v Speaker 3>to be weekly. At Black OAR, we're being very efficient,

0:33:48.920 --> 0:33:51.280
<v Speaker 3>not just because of the burn of the cash burn,

0:33:51.280 --> 0:33:54.440
<v Speaker 3>but also an organization is becoming less efficient when you

0:33:54.480 --> 0:33:56.840
<v Speaker 3>have too many people. That's something we would like to

0:33:56.920 --> 0:33:58.040
<v Speaker 3>keep as long as possible.

0:33:58.760 --> 0:34:01.320
<v Speaker 5>Black or CEO Shanna, thank you so much for your

0:34:01.320 --> 0:34:03.960
<v Speaker 5>time here on Bloomberg Technology, thank you very much for

0:34:04.000 --> 0:34:04.360
<v Speaker 5>having me.

0:34:12.480 --> 0:34:15.400
<v Speaker 4>In a rare move by Apple, the company is hitting

0:34:15.400 --> 0:34:18.319
<v Speaker 4>pause on development of this next year's software updates for

0:34:18.360 --> 0:34:20.719
<v Speaker 4>the Iphoney, I've Pad, the Mac and other devices so

0:34:20.760 --> 0:34:22.800
<v Speaker 4>that they can actually root out glitches in the code,

0:34:23.040 --> 0:34:24.840
<v Speaker 4>none of them. Bloombog Technology is Mark goam and of

0:34:24.880 --> 0:34:28.000
<v Speaker 4>course broke the story and mark how out of line

0:34:28.080 --> 0:34:29.520
<v Speaker 4>with the usual policy? Is this for Apple?

0:34:30.719 --> 0:34:32.040
<v Speaker 7>Yeah, thank you so much for having me.

0:34:32.120 --> 0:34:32.279
<v Speaker 2>So.

0:34:32.440 --> 0:34:35.960
<v Speaker 1>In twenty nineteen, when iOS was as buggy as ever,

0:34:36.320 --> 0:34:40.879
<v Speaker 1>Craig Federigi, the senior vice president of software engineering at Apple, implemented.

0:34:40.320 --> 0:34:41.800
<v Speaker 7>What the company calls the Pact.

0:34:42.160 --> 0:34:46.360
<v Speaker 1>The idea from twenty nineteen until now is you cannot

0:34:46.360 --> 0:34:50.000
<v Speaker 1>add any new features into iOS during its development if

0:34:50.000 --> 0:34:52.480
<v Speaker 1>it's going to break other features or make the operating

0:34:52.520 --> 0:34:55.359
<v Speaker 1>system more buggy. So far in development of next year's

0:34:55.360 --> 0:34:57.560
<v Speaker 1>operating systems, Apple has found that a lot of that

0:34:57.640 --> 0:35:00.000
<v Speaker 1>is happening. A lot of new bugs are being introduced

0:35:00.200 --> 0:35:04.040
<v Speaker 1>the software. So last week they implemented a freeze, a

0:35:04.040 --> 0:35:06.720
<v Speaker 1>pause or a delay to the next stage of development

0:35:06.719 --> 0:35:09.080
<v Speaker 1>and the next software dates. So for one week, all

0:35:09.160 --> 0:35:13.520
<v Speaker 1>Apple's engineers can focus entirely on fixing bugs before moving forward.

0:35:14.640 --> 0:35:15.360
<v Speaker 7>Brilliant scoop.

0:35:15.600 --> 0:35:17.400
<v Speaker 4>We keep a track of all of it as always,

0:35:17.440 --> 0:35:19.040
<v Speaker 4>Mark Gumman, and we thank you so much for bringing

0:35:19.040 --> 0:35:21.640
<v Speaker 4>it today. Meanwhile, we want to talk about today's Bloomberg

0:35:21.680 --> 0:35:24.920
<v Speaker 4>Big Take. It's focusing on Elon Musk's neuralink as it

0:35:24.960 --> 0:35:27.759
<v Speaker 4>seeks a volunteer for its first clinical trial. Meaning look,

0:35:27.760 --> 0:35:29.800
<v Speaker 4>it's looking for someone willing to have a chunk of

0:35:29.840 --> 0:35:32.680
<v Speaker 4>their skull removed by a surgeon so a large robot

0:35:32.840 --> 0:35:35.439
<v Speaker 4>can insert a series of electrodes super thin wires into

0:35:35.440 --> 0:35:38.359
<v Speaker 4>their brain. Now, when the robot finishes, the missing piece

0:35:38.400 --> 0:35:40.720
<v Speaker 4>of skull will be replaced with a computer the size

0:35:40.840 --> 0:35:43.640
<v Speaker 4>of personally about a quarter and that's meant to stay

0:35:43.640 --> 0:35:46.000
<v Speaker 4>there for years. It's job will we be to read

0:35:46.080 --> 0:35:49.160
<v Speaker 4>and analyze the person's brain activity, then relay that information

0:35:49.320 --> 0:35:52.280
<v Speaker 4>wirelessly to a nearby laptop or tablet.

0:35:53.440 --> 0:35:54.239
<v Speaker 7>We volunteering.

0:35:55.360 --> 0:35:58.560
<v Speaker 4>Actually, what's notable is the ideal candidate is under forty,

0:35:58.640 --> 0:36:00.759
<v Speaker 4>like yourself. But actually our lives.

0:36:00.840 --> 0:36:04.720
<v Speaker 5>Really I'm not volunteering, but yeah, okay. Sticking with Musk,

0:36:05.040 --> 0:36:08.680
<v Speaker 5>his sprawling business empire has granted the billionaire a degree

0:36:08.719 --> 0:36:13.640
<v Speaker 5>of power and global influence. That transcends the industry to reshaped.

0:36:14.000 --> 0:36:17.560
<v Speaker 5>In a new weekly chat show and podcasts, Bloomberg editors

0:36:17.600 --> 0:36:20.800
<v Speaker 5>and reporters break down the most important stories en Musk

0:36:21.080 --> 0:36:23.520
<v Speaker 5>and his empire. We're joined now by two of them,

0:36:23.640 --> 0:36:26.440
<v Speaker 5>Bloomberg's Sarah Fryar and Max Chafkin.

0:36:27.120 --> 0:36:28.960
<v Speaker 7>Elon Inc.

0:36:30.000 --> 0:36:31.799
<v Speaker 5>You We've actually all been talking about this for quite

0:36:31.800 --> 0:36:33.759
<v Speaker 5>a while, but I guess I'm with you, Sarah. What

0:36:33.840 --> 0:36:35.600
<v Speaker 5>is the broad concept of Elon Inc?

0:36:36.239 --> 0:36:38.080
<v Speaker 12>This is, this is a man you can no longer

0:36:38.160 --> 0:36:42.239
<v Speaker 12>think of just in terms of his disparate company there

0:36:42.239 --> 0:36:45.680
<v Speaker 12>are six of them now, But he's also a geopolitical force.

0:36:46.080 --> 0:36:52.200
<v Speaker 12>He affects our culture. He owns a major communication platform.

0:36:52.719 --> 0:36:56.000
<v Speaker 12>He's in the car business, he's in satellites, you know,

0:36:56.400 --> 0:36:57.360
<v Speaker 12>involved in wars.

0:36:57.680 --> 0:37:00.440
<v Speaker 7>He has this power that is.

0:37:00.239 --> 0:37:05.319
<v Speaker 12>So so vast and so interconnected that we felt that,

0:37:05.680 --> 0:37:10.560
<v Speaker 12>you know, even as he becomes a more overexposed person,

0:37:10.640 --> 0:37:16.799
<v Speaker 12>perhaps directing media with his own messages, he's under explained.

0:37:17.760 --> 0:37:21.640
<v Speaker 12>His work is something that you know, takes some journalists

0:37:21.640 --> 0:37:23.719
<v Speaker 12>who actually spoken to a lot of the people in

0:37:23.800 --> 0:37:26.719
<v Speaker 12>the in the Musk Empire and who can tell you

0:37:26.719 --> 0:37:27.560
<v Speaker 12>what's really going on.

0:37:28.160 --> 0:37:31.080
<v Speaker 4>Ashley Vans of course, writes today's big take, which is

0:37:31.120 --> 0:37:34.360
<v Speaker 4>about neuralink, and I mean, the opportunities of helping particularly

0:37:34.360 --> 0:37:37.000
<v Speaker 4>people who are paralyzed, are extraordinary. But of course many

0:37:37.000 --> 0:37:39.560
<v Speaker 4>would then say for that, you need trust, you need safety,

0:37:39.640 --> 0:37:42.200
<v Speaker 4>not to mention with spaceships which blow up every now

0:37:42.200 --> 0:37:45.080
<v Speaker 4>and then cars as well. Where are you starting with

0:37:45.160 --> 0:37:47.520
<v Speaker 4>the enormous empire with which he oversees.

0:37:47.680 --> 0:37:49.759
<v Speaker 13>Well, the show that will drop just in a few

0:37:49.760 --> 0:37:52.920
<v Speaker 13>hours today, it's about groc, which is the new chat

0:37:52.960 --> 0:37:57.719
<v Speaker 13>bot created by x dot ai Elon Musk's AI company,

0:37:57.880 --> 0:37:59.640
<v Speaker 13>and I think it's a it's a really good example

0:37:59.680 --> 0:38:01.759
<v Speaker 13>of what Sarah was just talking about. You know, x

0:38:02.040 --> 0:38:06.399
<v Speaker 13>dot ai standalone tech company. They've recruited some really big

0:38:06.719 --> 0:38:10.160
<v Speaker 13>names from DeepMind and Google, but in certain ways it's

0:38:10.239 --> 0:38:13.040
<v Speaker 13>very closely connected to X you know, formerly known as Twitter,

0:38:13.280 --> 0:38:16.000
<v Speaker 13>because Musk is sort of using it as an enticement

0:38:16.200 --> 0:38:18.840
<v Speaker 13>to sign up for this new higher tier of service,

0:38:19.040 --> 0:38:21.800
<v Speaker 13>which I believe is called Premium Plus. It's it costs

0:38:21.840 --> 0:38:24.239
<v Speaker 13>between sixteen and twenty two dollars a month, and Musk

0:38:24.360 --> 0:38:26.480
<v Speaker 13>is kind of saying, if you do this, you'll get

0:38:26.520 --> 0:38:29.000
<v Speaker 13>all the great benefits of X Premium Plus but you'll

0:38:29.040 --> 0:38:32.040
<v Speaker 13>also get my AI company. So again it's hard to

0:38:32.400 --> 0:38:34.040
<v Speaker 13>understand those companies stepple On Inc.

0:38:34.040 --> 0:38:37.120
<v Speaker 5>Though, Right, these companies all have a direct relationship, and

0:38:37.200 --> 0:38:39.200
<v Speaker 5>also we're good value here, right, So you get a

0:38:39.200 --> 0:38:42.680
<v Speaker 5>bonus episode dropping, which I think is about musk relationship

0:38:42.719 --> 0:38:45.440
<v Speaker 5>with unions. I find that fascinating because I remember covering

0:38:45.480 --> 0:38:49.080
<v Speaker 5>Tesla March twenty twenty onwards when the pandemic hit and

0:38:49.239 --> 0:38:52.920
<v Speaker 5>the union sensed an opportunity because of must position on

0:38:52.960 --> 0:38:54.719
<v Speaker 5>the Fremont factory at the time. But tell us about

0:38:54.719 --> 0:38:55.279
<v Speaker 5>episode two.

0:38:55.520 --> 0:38:58.360
<v Speaker 13>Yeah, so we actually have two segments on the first episode.

0:38:58.360 --> 0:39:00.680
<v Speaker 13>Episode two is a bonus episode with an Ashley Vance

0:39:01.280 --> 0:39:04.680
<v Speaker 13>on Neuralink getting into the exclusive some exclusive detail about

0:39:04.680 --> 0:39:07.200
<v Speaker 13>what we learned or what Ashley learned getting inside of

0:39:07.280 --> 0:39:10.640
<v Speaker 13>that company on the union issue. It is super interesting

0:39:10.680 --> 0:39:13.440
<v Speaker 13>because of course it gets down to Tesla's finances, but

0:39:13.480 --> 0:39:16.719
<v Speaker 13>it also has a serious political component. You know, Sean Fain,

0:39:17.160 --> 0:39:20.200
<v Speaker 13>the leader of the UAW, is really a new kind

0:39:20.239 --> 0:39:23.040
<v Speaker 13>of union boss and in certain ways, you know, there's

0:39:23.080 --> 0:39:26.440
<v Speaker 13>a thought that maybe Elon Musk and Sean Fayne are

0:39:26.520 --> 0:39:28.880
<v Speaker 13>matched to each other. They're both sort of larger than life.

0:39:28.920 --> 0:39:31.560
<v Speaker 13>They both have big ambitions. We will see what happens.

0:39:32.080 --> 0:39:35.239
<v Speaker 4>Sarah, Ultimately, what do you want and listener to come

0:39:35.280 --> 0:39:38.600
<v Speaker 4>away with? Because many would say we've had too much

0:39:38.760 --> 0:39:42.759
<v Speaker 4>elon news across every outlet of late what's different here?

0:39:42.800 --> 0:39:44.920
<v Speaker 4>How do you think you really educate in that manner?

0:39:45.040 --> 0:39:47.920
<v Speaker 12>I mean, I think we want to explain, right, and

0:39:47.560 --> 0:39:51.279
<v Speaker 12>I think these two pieces of news that you may

0:39:51.320 --> 0:39:57.479
<v Speaker 12>have heard of, the groc Ai and the union fight

0:39:57.560 --> 0:40:01.120
<v Speaker 12>that may be coming up, they both are are connected

0:40:01.160 --> 0:40:04.160
<v Speaker 12>to Musk as a geopolitical figure and how he might

0:40:04.160 --> 0:40:07.840
<v Speaker 12>affect the twenty twenty four elections, how he's talking with

0:40:07.880 --> 0:40:11.319
<v Speaker 12>the governments around the world, what might be motivating him, right,

0:40:11.440 --> 0:40:15.160
<v Speaker 12>and so we go into like his conflicts and his

0:40:15.280 --> 0:40:19.640
<v Speaker 12>competition and what really drives must And so we get

0:40:19.840 --> 0:40:24.879
<v Speaker 12>sort of behind the news and explain why.

0:40:24.160 --> 0:40:26.280
<v Speaker 4>You should care, what you should care about.

0:40:26.480 --> 0:40:30.960
<v Speaker 12>And what to look forward to as the world shifts

0:40:30.960 --> 0:40:33.399
<v Speaker 12>around this extremely powerful man.

0:40:33.760 --> 0:40:39.120
<v Speaker 4>Context Bloomberg, Max Chafkin, Seraphra really excited further, not one,

0:40:39.160 --> 0:40:42.240
<v Speaker 4>but two that you're going to be getting dropping whichever

0:40:42.560 --> 0:40:47.080
<v Speaker 4>cause area that you currently are digesting your podcast, because

0:40:47.080 --> 0:40:48.960
<v Speaker 4>we've got many ed But meanwhile, I doesn't have this

0:40:49.120 --> 0:40:50.120
<v Speaker 4>edition of Any Bad Techology.

0:40:50.120 --> 0:40:52.160
<v Speaker 5>Don't forget to check out our podcasts wherever you get

0:40:52.160 --> 0:40:53.920
<v Speaker 5>yours from New York Businesses, Bloomberg